Adaptivity is a challenging open issue in data stream management. In this paper, we tackle the problem of memory adaptivity inside a system executing temporal sliding window queri...
We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observations in partially observable en...
Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framew...
—This paper presents a system-level Network-on-Chip modeling framework that integrates transaction-level model and analytical wire model for design space exploration. It enables ...
— Currently there are different approaches to develop fault-tolerant embedded software: implementing the system from scratch or using libraries respectively specialized hardware....